[英]gtrendsR geo MSA/Area Code
I am gathering Google Trends data using the R Package gtrendsR. 我正在使用R Package gtrendsR收集Google趋势数据。 I am trying to pull data for each metropolitan statistical area (MAS) but area code would also be good. 我正在尝试为每个大都市统计区域(MAS)提取数据,但区域代码也会很好。 So far I have only managed to get the state-level data. 到目前为止,我只是设法获得州级数据。 Here is the code for that. 这是代码。
example <- gtrends("car", geo="US-FL")$interest_over_time
I have tried the following for the MSA: 我为MSA尝试了以下内容:
example2 <- gtrends("car", geo="US-FL-Jacksonville FL")$interest_over_time
and for the area codes: 并为区号:
example3 <- gtrends("car", geo="US-FL-904")$interest_over_time
I get errors saying that the package cannot retrieve valid codes. 我收到错误,说包裹无法检索有效代码。 In data("countries") associated with the package, the codes are only for state-level - eg US-FL for Florida. 在与包相关联的数据(“国家”)中,代码仅用于州级 - 例如佛罗里达州的US-FL。
I would be interested in knowing how I can retrieve more granular data with this package, along the lines described in example2 and example3 above. 我有兴趣知道如何使用此包检索更细粒度的数据,沿着上面的example2和example3中描述的行。
To retrieve data for "Jacksonville, FL", you should use geo = "US-FL-561"
: 要检索“Jacksonville,FL”的数据,您应该使用geo = "US-FL-561"
:
example2 <- gtrends("car", geo = "US-FL-561")$interest_over_time
To find the geo code for cities, you can use this code (you can replace "US-FL"
by any country-states code you want): 要查找城市的地理代码,您可以使用此代码(您可以使用您想要的任何国家/地区代码替换"US-FL"
):
data("countries")
codes <- unique(countries$sub_code[substr(countries$sub_code, 1,5) == "US-FL"])
codes
#[1] US-FL US-FL-571 US-FL-592 US-FL-561 US-FL-528 US-FL-534 US-FL-656 US-FL-539 US-FL-548 US-FL-530
countries[countries$sub_code %in% codes[2:length(codes)],]
# country_code sub_code name
#122665 US US-FL-571 Ft. Myers-Naples, FL
#122666 US US-FL-592 Gainesville, FL
#122667 US US-FL-561 Jacksonville, FL
#122668 US US-FL-528 Miami-Ft. Lauderdale, FL
#122670 US US-FL-534 Orlando-Daytona Beach-Melbourne, FL
#122671 US US-FL-656 Panama City, FL
#122672 US US-FL-539 Tampa-St Petersburg (Sarasota), FL
#122673 US US-FL-548 West Palm Beach-Ft. Pierce, FL
#122680 US US-FL-530 Tallahassee, FL-Thomasville, GA
If easier, you can also write the code as a function: 如果更简单,您还可以将代码编写为函数:
city_code <- function(geo){
codes <- unique(countries$sub_code[substr(countries$sub_code, 1,5) == geo])
if(length(codes) > 1){
countries[countries$sub_code %in% codes[2:length(codes)], 2:3]
} else{
message('No city code for this geo')
}
}
city_code("US-AL")
# sub_code name
#122636 US-AL-630 Birmingham, AL
#122637 US-AL-606 Dothan, AL
#122638 US-AL-691 Huntsville-Decatur (Florence), AL
#122639 US-AL-698 Montgomery (Selma), AL
#122669 US-AL-686 Mobile, AL-Pensacola (Ft. Walton Beach), FL
city_code("US-CA")
# sub_code name
#122649 US-CA-800 Bakersfield, CA
#122650 US-CA-868 Chico-Redding, CA
#122651 US-CA-802 Eureka, CA
#122652 US-CA-866 Fresno-Visalia, CA
#122653 US-CA-803 Los Angeles, CA
#122654 US-CA-828 Monterey-Salinas, CA
#122655 US-CA-804 Palm Springs, CA
#122656 US-CA-862 Sacramento-Stockton-Modesto, CA
#122657 US-CA-825 San Diego, CA
#122658 US-CA-807 San Francisco-Oakland-San Jose, CA
#122659 US-CA-855 Santa Barbara-Santa Maria-San Luis Obispo, CA
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